NetKet: A machine learning toolkit for many-body quantum systems
Giuseppe Carleo,
Kenny Choo,
Damian Hofmann,
James E.T. Smith,
Tom Westerhout,
Fabien Alet,
Emily J. Davis,
Stavros Efthymiou,
Ivan Glasser,
Sheng-Hsuan Lin,
Marta Mauri,
Guglielmo Mazzola,
Christian B. Mendl,
Evert van Nieuwenburg,
Ossian O’Reilly,
Hugo Théveniaut,
Giacomo Torlai,
Filippo Vicentini,
Alexander Wietek
Affiliations
Giuseppe Carleo
Center for Computational Quantum Physics, Flatiron Institute, 162 5th Avenue, NY 10010, New York, USA; Corresponding author.
Kenny Choo
Department of Physics, University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
Damian Hofmann
Max Planck Institute for the Structure and Dynamics of Matter, Luruper Chaussee 149, 22761 Hamburg, Germany
James E.T. Smith
Department of Chemistry, University of Colorado Boulder, Boulder, CO 80302, USA
Tom Westerhout
Institute for Molecules and Materials, Radboud University, NL-6525 AJ Nijmegen, The Netherlands
Fabien Alet
Laboratoire de Physique Théorique, IRSAMC, Université de Toulouse, CNRS, UPS, 31062 Toulouse, France
Emily J. Davis
Department of Physics, Stanford University, Stanford, CA 94305, USA
Stavros Efthymiou
Max-Planck-Institut für Quantenoptik, Hans-Kopfermann-Straße 1, 85748 Garching bei München, Germany
Ivan Glasser
Max-Planck-Institut für Quantenoptik, Hans-Kopfermann-Straße 1, 85748 Garching bei München, Germany
Sheng-Hsuan Lin
Department of Physics, T42, Technische Universität München, James-Franck-Straße 1, 85748 Garching bei München, Germany
Marta Mauri
Center for Computational Quantum Physics, Flatiron Institute, 162 5th Avenue, NY 10010, New York, USA; Dipartimento di Fisica, Università degli Studi di Milano, via Celoria 16, I-20133 Milano, Italy
Guglielmo Mazzola
Theoretische Physik, ETH Zürich, 8093 Zürich, Switzerland
Christian B. Mendl
Technische Universität Dresden, Institute of Scientific Computing, Zellescher Weg 12-14, 01069 Dresden, Germany
Evert van Nieuwenburg
Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA 91125, USA
Ossian O’Reilly
Southern California Earthquake Center, University of Southern California, 3651 Trousdale Pkwy, Los Angeles, CA 90089, USA
Hugo Théveniaut
Laboratoire de Physique Théorique, IRSAMC, Université de Toulouse, CNRS, UPS, 31062 Toulouse, France
Giacomo Torlai
Center for Computational Quantum Physics, Flatiron Institute, 162 5th Avenue, NY 10010, New York, USA
Filippo Vicentini
Université de Paris, Laboratoire Matériaux et Phénomènes Quantiques, CNRS, F-75013, Paris, France
Alexander Wietek
Center for Computational Quantum Physics, Flatiron Institute, 162 5th Avenue, NY 10010, New York, USA
We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques. The framework is built around a general and flexible implementation of neural-network quantum states, which are used as a variational ansatz for quantum wavefunctions. NetKet provides algorithms for several key tasks in quantum many-body physics and quantum technology, namely quantum state tomography, supervised learning from wavefunction data, and ground state searches for a wide range of customizable lattice models. Our aim is to provide a common platform for open research and to stimulate the collaborative development of computational methods at the interface of machine learning and many-body physics. Keywords: Neural-network quantum states, Variational Monte Carlo, Quantum state tomography, Machine learning, Supervised learning
